Machine Learning, Artificial Intelligence, and Data ScienceKDE Topic Area
In our new episode of Machine Learning, Artificial Intelligence, and Data Science in the Knowledge-Driven Enterprise, we discuss business drivers, future vision, technologies, and the framework supporting proactive user experiences in information technology.
In this episode of the Knowledge-Driven Podcast, MITRE’s Tamara Ambrosio-Hemphill guides us through the decision-support tools MITRE has been developing to help the U.S. safeguard critical infrastructure across the globe while building partnerships and helping allies create a better future for their citizens.
Blockchain is everyone’s favorite buzz word. Whether it’s crypto currency or NFTs, the technology has been getting a lot of attention for how it could disrupt how we buy and interact. But MITRE’s own Jaya Tripathi sees a far more critical use for blockchain, tracking drugs. Listen in as she shares her vision of a brighter pharmaceutical future built on Blockchains.
Have you ever wondered how Google Maps works? How does the application calculate the fastest route for you, and how does it do this so quickly? The answer might surprise you – at the backbone of these navigational applications and software is graph theory.
According to the independent website Government Technology, “Many government agencies have an unstructured data nightmare, with bits and bytes scattered across servers, clouds and hypervisors.” Vernor Vinge calls this so-called nightmare “data glut.” In an era of Internet of Things (IoT), the data glut may not get better, and, in fact, is likely to get worse.
As data science has exploded in popularity and use, so have the tools used to solve problems in the domain. Some of these open source programs and programming suites have become extremely popular, and thus developers have designed a whole host of code that might be referred to as add-ons, extensions, and packages to improve functionality and save time for users, both experienced and new.
So You Want to Think Like a Data Scientist? The Importance of Visualizations in the Data Science Workflow
Although the moniker data scientist implies that the role centers around manipulating data and modeling it, visualizing data and creating visualizations are an integral part of the daily workflow for practicing data scientists like me. Not only do visualizations allow us to communicate results quickly and efficiently, but visualizing is a key tool during exploratory data analysis, data cleaning, modeling, and other steps of the process of telling stories with numbers.
In November 2019, a customer of Apple Card complained when he and his wife separately applied for Apple Card credit, and his wife was given a credit limit twenty times lower than his despite the fact that they jointly owned all assets in a community property state. According to Neil Vigdor in an article in the New York Times, an Apple Card representative checked into the matter and came back with the explanation that “It was the algorithm.”
MITRE has taken on a challenge: to shape America’s future workforce and economy by alerting college students to the power of artificial intelligence (AI). That vision is now taking shape at schools across the country through an initiative known as Generation AI Nexus (Gen AI).
Instead of hitting the beach over the third weekend in September, more than 1,000 students from several Florida and southeast universities loaded up on caffeine, went without sleep, and were driven by a “will to do good.”
A woman who has always identified as Ashkenazi Jewish received a DNA testing kit from one of the ancestry services and participates on a whim. Surprise! Turns out her father was actually a non-Jewish sperm donor. It’s one of many fascinating and recent cases of renegotiating identity, along with stories about an adopted child finding their true birth family, or even individuals tracing their ancestry back to someone practicing witchcraft.
Dr. Philip Barry is the Technical Director for Modeling, Simulation, Experiments, & Analysis here at MITRE. When he’s not leading simulations work, he is teaching Risk Management at George Mason. Ever focused on bringing new tools and methodologies into the classroom, Dr. Barry partnered with George Mason and Joe Garner and Ali Zaidi from MITRE’s Generation AI Nexus (Gen AI) team, to create a first-of-its-kind lesson blending risk management with artificial intelligence (AI).
Ali Zaidi is a MITRE data scientist tackling an interesting challenge for MITRE as part of his work for Generation AI Nexus. As the fields of machine learning and data science have grown, the need for machine learning education has become a necessity of many fields few would associate with computer science.
Jesse Buonanno, a Cyber Security Engineer at MITRE, focuses on cyber operations. Jesse spends his spare time learning about blockchain and cryptocurrencies.
Congratulations! You’ve built your self-driving car! Now what? Take it out for a spin on that cross-country trip, watching movies and the landscape as you go from sea to shining sea?
So you’ve heard about Symphony™ – MITRE’s automated provisioning framework that rapidly builds secure analytic cells for geospatial, AI, and machine learning applications. Have you tried explaining it to a college student?
Welcome to the second installment of the Knowledge-Driven Podcast. In this series, Software Systems Engineer Cameron Boozarjomehri interviews technical leaders at MITRE who have made knowledge sharing and collaboration an integral part of their practice.
Clinical diagnostic support. Loan approval. Predictive policing and parole. These are all examples of consequential systems, meaning that they are systems with immediate, long-term, impactful consequences on people within them.
Science is “the systematic study of the structure and behavior of the physical and natural world through observation and experimentation.” Since its emergence during the late renaissance, scientific progress has been made primarily through the aptly named scientific method.
Artificial intelligence (AI) is getting better all the time. You can see it all around you, from Alexa and Siri keeping your appointments and shopping lists, to news articles about self-driving cars, to a program called AlphaZero (Silver et al., 2018) that will probably never lose to any human in chess or GO.
How do we prepare for the inevitable change in the world today? How do we take into account not just the way the world is now – but the way it looks in the future?
Somewhere on a whiteboard in a classroom at the Universities of Shady Grove, swims a fish. Drawn in black marker, complete with a fedora, sunglasses, and a goatee, the sketchy-looking ichthyoid intones into a word bubble…
Is artificial intelligence (AI) the way of the future… or already the way of the present?
Applications of AI surround us in our daily lives – ever use an app to get around traffic? How about checking your social media feeds? As our society integrates AI into our daily lives, it’s important to note that the upcoming generation has always lived with AI.
The process of neural interactions and visual interpretation happens every time your brain wants to identify literally anything you look at.
Imagine waiting 30 minutes or longer to get through to a customer service center and when your call is finally answered, you can’t understand what the service representative is saying because you have a hearing impairment. Or you place a call to your doctor but aren’t able to communicate your needs to the medical staff because your speech is impaired. Or you are a child with autism and being in a classroom and interacting with your teacher and classmates overwhelms you with anxiety.
You are part of the design team tasked with implementing a modern chip-and-PIN ATM system for your newest customer: a large banking chain. The bank has contracted an outside firm to design the user interface of the ATM and wants to maintain the existing bank administration software they currently use…
Blockchain has essentially created a new type of internet in which information can be widely distributed without relying on traditional centralized architectures…
What Does It Mean for Artificial Intelligence to Achieve Parity with a Human? A Case Study of Neural Machine Translation
On March 12, 2018, Microsoft’s Artificial Intelligence (AI) and Research group announced: “We find that our latest neural machine translation system has reached a new state-of-the-art, and that the translation quality…
Autonomy is a broad and complex topic, overlapping with artificial intelligence, unmanned systems, and human domains, with each domain needing to leverage …
Intelligent voice assistants like Amazon’s Echo and Google Home are quickly becoming an integral part of our daily lives. Echo adoption …
When you hear the word autonomy, it tends to be in the context of autonomous vehicles – self-driving cars that take passengers wherever they need to go without the need for human input…
MITRE believes that data is the next medical innovation in health. How might connecting people and data reinvent the health experience? To find out, a team of researchers developed Home Assessments for Prompt Intervention (HAPI), a serious game that uses Microsoft Kinect-based joint tracking to detect critical changes in patients with cerebral palsy…
Applications in Data Science: Anti-Fragility in Action…
Data Science Practitioners…
An electronic flight bag (EFB) may typically be used to replace pilots’ paper charts, but with some new developments, it could start to be more of a cognitive assistant. MITRE researchers….
There’s thinking about, talking about, and doing, and they all have a time and place in any domain. With data science, though, doers rule. A big bucket of ostensibly random stuff in the hands of a skilled practitioner becomes the stuff of art. Yup, even data about a fire hydrant.—Editor
In her previous post, Technical Challenges in Data Science, Amanda Andrei discussed the need for technical vigilance and with experts Dr. Elizabeth Hohman, statistician and group leader within MITRE’s Department of Data Analytics, and Dr. Eric Bloedorn, senior principal artificial intelligence engineer. Tools and models, however carefully managed, tell, of course, only part of the story. Data scientists are people, and they and the tools they use reside within organizational cultures, which may require as much training as the data to hand.—Editor
As Amanda Andrei mentioned in her previous post, Defining, Applying, and Coordinating Data Science at MITRE, we are generating 2.5 million terabytes of data a day, and the need for data science teams and individual contributors is crucial for moving what we find up the spectrum to knowledge that we might usefully….
For a long time I have thought I was a statistician, interested in inferences from the particular to the general, wrote mathematician….
It’s both less scary and more thrilling than you might think—and we’ve been living with nascent versions of machine learning for some time in the form of cognitive assistance tools. Spellcheck, for example, and the suggestions for replies that Gmail now displays are...
With the increasing demand for a diversity of new entrant operations come a variety of economic efficiency challenges. A MITRE team collaborating with the Federal Aviation Administration and industry experts is developing technology to make it easier for FAA’s air...
I was recently reminded of a quote by John Adams: “Facts are stubborn things; and whatever may be our wishes, our inclinations, or the dictates of our passions, they cannot alter the state of facts and evidence”.
My son recently started middle school and went through a library orientation. At the end of the orientation, the students had to take a quiz to prove that they could find their way around the library. My son failed the quiz.
Authors: Marilyn Kupetz and Angela O’HanlonMITRE’s Analysis Tool Shed Lab (ATS) program is one of MITRE’s largest R&D Labs, focusing on analysis capability and analytic activity. Lab users acquire hands-on experience with 100+ tools and datasets, MITRE prototypes,...
MITRE’s knowledge-sharing culture incorporates many kinds of resources, from human, to traditional paper, to digital. For researchers like Melissa Dolph, the wealth of resources enables her to perform on behalf of customers and to network, both of which enrich her...